Channel estimation plays an important role in accurate demodulation of symbols received at Orthogonal Frequency Division Multiplexing (OFDM) receivers. OFDM receivers may obtain an estimate of a physical channel, which may be characterized by the channel impulse response (CIR). The channel estimate may reflect distinct channel characteristics such as Doppler shift, path-loss, phase shifts, multipath propagation, noise and other interference, etc.
Signals transmitted over such wireless channels may inherently be modified and/or altered as a result of transmission over the corresponding wireless channel, and accordingly received symbols may be corrupted. Accurate channel estimation at an OFDM receiver may allow the OFDM receiver to inverse any such corruption of received symbols, thereby leading to improved reception performance.
Conventional Long Term Evolution (LTE) networks as specified by the 3rd Generation Partnership Project (3GPP) specify utilization of OFDM for downlink transmissions from base stations to connected mobile terminals. Mobile terminals operating on LTE networks therefore attempt to obtain channel estimates for active downlink channels in order to accurately estimate demodulate received symbols by compensating for channel effects.
Precise channel estimates may in effect lead to improved demodulation of received signals. In order to facilitate effective channel estimation, a base station may transmit pilot (or reference) symbols to be received by mobile terminals. The pilot symbols may be predetermined, and accordingly a mobile terminal may analyze a received pilot symbol in order to determine any differences impressed on the symbol by the wireless channel. The mobile terminal may then obtain a channel estimate by comparing the received pilot symbol to the predetermined pilot symbols, and may compensate for channel effects based on the channel estimate.
Channel estimates may therefore be useful in approximating the effects a wireless channel will have on a transmitted wireless signal. In addition to potential benefits involved during real-time operation of a wireless communication network, accurate channel estimates may be valuable in offline testing and analysis of wireless networks.
One such example is in Virtual Drive Testing (VDT), which involves methods for replaying drive tests in a controlled lab environment. VDT may act as a substitute for physical drive tests, which involve taking physical measurements of wireless signals at various locations around a physical base station in order to evaluate network performance. VDT may thus provide a cost-effective alternative to physical drive tests, as VDT may be performed in a laboratory environment as opposed to field testing. VDT may also assist in decreasing time to market for new equipment, as debugging in a lab environment may be greatly simplified compared to debugging during field testing. Finally, VDT may help to optimize field performance.
In order to act as a suitable substitute for field testing, VDT must be able to “replay” a wireless channel with a high degree of accuracy. The wireless channel may thus be recreated in a lab environment such as an Over The Air (OTA) chamber, where testing may be performed using the recreated wireless channel. Effective channel recreation therefore requires an accurate estimate of the channel impulse response, which in effect may act as the channel estimate.
The channel impulse response may be estimated based on OFDM pilot symbols recorded in the field. As opposed to performing multiple drive tests, the field measurement of the pilot symbols may only need to be performed once. A channel impulse response may then be derived from the recorded pilot symbols and may subsequently be used as the channel estimate for recreation in a VDT environment.
Sparse channel estimation, particularly sparse Bayesian learning, has proven to be an effective method for obtaining highly accurate channel impulse response estimates. However, as will be later described in further detail, such sparse Bayesian channel estimation methods involve discretizing the delay domain. In other words, the delay domain may be reduced from the continuous domain in real-world applications to a finite discrete grid of points. While the delay domain discretization may be advantageous in reducing processing requirements for estimating channel impulse responses, the discretization may introduce a “model mismatch” (i.e. significant differences between the channel impulse response estimate and the actual channel impulse response), which may result in performance degradation in VDT environments.
Improved performance of VDT may be realized by utilizing a channel estimation method that considers a continuous-domain delay space as opposed to a discretized delay space. Such a continuous domain-delay channel estimation method may provide significant improvements over a discretized-domain delay method, and may estimate the number of multipath components as well as their associated parameters (delays and gains) with high accuracy.